It is well known in the prior art to use mechanical ventilation in patients who are unable maintain suitable arterial oxygen partial pressure and pH without assistance. In early ventilators, mechanical or pneumatic systems (e.g. piston pump, bellows) controlled the amplitude and timing of flow or pressure of gas delivered to the patient. Ventilatory rate and flow or pressure were typically adjusted manually. Later, servo-controlled proportional gas delivery valves were introduced; because these valves are electronically controlled, computer control of ventilator function became possible. In most currently available mechanical ventilators, the volume, flow, pressure and/or timing of ventilation are controlled by computer.
Because various factors influence the response of the patient to mechanical ventilation, simply regulating ventilator parameters does not necessarily maintain the patient's arterial pH or oxygen partial pressure at the desired levels. Monitoring systems with integrated alarms and alerts have been developed to notify the clinician if patient parameters are not within the target range during mechanical ventilation. For example, such a system has been patented by Mick et al. (U.S. Pat. No. 5,355,893, which is incorporated herein by reference). This type of system alerts the clinician when there is a problem, but offers no guidance in resolving the problem.
A number of systems have been developed which provide for closed-loop, filly-automated control of ventilation. Examples are systems reported by Bhutani et al. (Pediatric Pulmonology, Vol. 14, pp. 110-117, 1992), East (Principles and Practice of Mechanical Ventilation, Ch. 12, McGraw-Hill, Inc., 1994), Morozoff and Evans (Biomed. Instr. Tech., Vol. 26, pp. 117-223, 1992), Rudowski et al.(Comp. Meth. Progr. Biomed, Vol. 31, pp. 33-42, 1990), and Tehrani (Ann. Biomed. Engr., Vo. 20, pp. 547-558, 1992), all of which are incorporated herein by reference. In general, these systems maintain a single patient parameter at a desired constant value, by modulating a single parameter of ventilation, while holding other parameters constant. For example, oxygen saturation may be regulated by adjusting the percent oxygen in the inspired gas mixture, or end tidal CO.sub.2 may be regulated by controlling the frequency of ventilation, while holding the breath size constant. While this approach is often effective, in some cases modulating a single parameter is insufficient to maintain oxygenation or pH at the desired level. None of the currently available closed-loop systems is capable of supporting a patient who has a serious respiratory disorder and who is in need of prolonged ventilatory support.
Expert systems make it possible to make decisions or classification of data in an automated fashion, by applying "expert knowledge" to the analysis of data. Expert systems may be structured in a number of ways. For example, a rule-base (decision tree) system uses expert-derived rules to generate a single solution for a given set of input data. Bayesian systems take into account the probabilistic nature of many decisions, and generate multiple possible solutions, each with an associated probability of correctness (for example, Altschuler et al., U.S. Pat. Nos. 4,839,822 and 5,005,143, incorporated herein by reference). Other expert systems are not set up with explicit rules, but rather are trained to make decisions by being presented with various sample data sets and the associated correct decision (according to one or more experts). Examples of such "learning systems" are described in Saito and Nakano (Proceedings, IEEE Inter. Conf. on Neural Networks, Jul. 24-27, 1988) and Gallant (U.S. Pat. No. 4,730,259, incorporated herein by reference).
Decision support tools which are also expert systems have been developed for use in various areas of medicine. One prior art application of expert systems is to make a diagnosis on the basis of a set of facts pertaining to the patient. Such a system is presented by Barnett et al. (JAMA, Vol. 258, pp. 67-74, 1987). Potter et al. (U.S. Pat. No. 4,733,354) describe a system for making a dermatological diagnosis. Adrion et al. (U.S. Pat. No. 5,023,785) describe an expert system for making diagnoses in the field of hematology. Suto et al. (U.S. Pat. No. 4,731,725) describe a system for making a diagnosis on the basis of medical images. Systems designed for use in pulmonary medicine include those of Klar and Zaiss (Lung, Suppl. pp. 1201-1209, 1990), Miksch et al. (Proc. 4th Conf. Art. Intell. in Med. Europe, October 1993, pp. 1-18), and Shahsavar et al. (Comp. Meth. Prog.Biomed., vol. 34, pp. 115-123, 1991). While such systems show promise, there are still many drawbacks which need to be resolved (Kassirer, New Eng. J. Med., Jun. 23, 1994, pp. 1824-1825). This and the preceding journal articles and patents are incorporated herein by reference.
Other expert systems generate suggestions of treatments for various medical problems, such as physical trauma (Dormond et al., U.S. Pat. No. 4,839,822, incorporated herein by reference). A few systems provide for integrated support in Critical (Intensive) Care (Higgens, Decision Support Systems in Critical Care, Ch. 24, Gardner and Shabot, Eds., Springer Verlag, 1994; Hunter, Int. J. Clin. Mon. and Comp., Vol. 8, pp. 189-199, 1991). Both of these texts are incorporated herein by reference.
Most decision support tools give instructions for patient care which are non-specific; for example, a list of possible treatments, each with an associated probability of correctness, or a single, general guideline for treatment, which must be interpreted and could be carried out in various ways depending on the personal preference of the clinician. In either case, the final decision on patient care is left to the discretion of the attending clinician. This is a drawback if it is necessary that care be delivered consistently to different patients, who may be under the care of different doctors. It is often desirable that instructions are provided which can be carried out by individuals who do not have authority to make patient treatment decisions independently (e.g. nurses, respiratory therapists, technician, etc.). In these cases, a system which generates executable instructions for patient care which are sufficiently detailed that they are not subject to interpretation by the person who carries them out would be preferable.
Expert systems are also available which provide guidance relating to the weaning of patients from mechanical ventilation. Examples are Burns et al. (Clin. Iss., Vol. 2, pp. 372-390, 1991), Dojat et al. (Int. J. Clin. Mon. Comp., vol. 9,pp. 239-250, 1992), Miksch (Proc. 4th Conf. Art. Intell. in Med. Europe, October 1993, pp. 1-8), and Strickland (Chest, Vol. 100, pp. 1096-1099, 1991) all of which are incorporated herein by reference.
Another approach for managing patient care is the use of "protocols" or decision trees in the form of paper-based flow diagrams. These also fall into the category of decision support tools. Protocols can be used in many areas of medicine. Protocols for handling a variety of disorders, including respiratory disorders, are published in Decision Making in Critical care (Hillary Don, Ed., B. C. Decker, Inc., Philadelphia, Pa., 1985) and Critical Care Algorithms (Armstrong et al, Eds., Oxford University Press, New York, 1991), both of which are incorporated herein by reference. Decision Making in Pulmonary Medicine (Karlinzky et al., Eds, B. C. Decker, Philadelphia, Pa., 1991) includes protocols which aid decision making in pulmonary medicine. These three texts are incorporated herein by reference. The protocols or decision trees published in these books provide sequential decision points and instructions for the diagnosis and treatment of patients. The instructions, however, are general and require interpretation by the physician. For example, in the protocol for "Weaning from Mechanical Ventilation" found on p. 124-125 of Decision making in Pulmonary Medicine, if the patient does not fulfill weaning criteria, the protocol advises the physician to "correct metabolic parameters". Although a list a possible metabolic problems is provided, no specific, executable instructions are provided for correcting these problems, or for identifying the underlying metabolic problem on the basis of the unfulfilled weaning criteria. Such protocols serve as guidelines for patient care but could only be used by someone with medical expertise who brings additional information and who is empowered to make judgements about care.
A different approach can be taken however. In a paper by East et al., (CHEST, Vol. 101, pp. 697-710, 1992, incorporated herein by reference) the use of a computerized protocol for the clinical management of Pressure Control Inverse Ratio Ventilation (PCIRV) is described. The protocol disclosed therein generates specific instructions for adjusting various parameters to regulated PCIRV. Morris and Gardner (Principles of Critical Care, Part II, Ch. 41, McGraw-Hill, Inc., 1992) provides an overview of computers in critical care, also incorporated herein by reference. As an example, a computerized protocol for inverse ratio ventilation (IRV) is presented. In the protocols described in both of these papers, detailed instructions for adjusting several parameters of ventilation are generated, and these serve as a set of dynamic standing orders for the clinical staff.
Virtually all currently available expert systems operate on a non-continuous basis. That is, when it is desired that the system generate a diagnosis or treatment advice, data are entered and the desired information generated. One system which does operate on a continuous basis is a system for delivery of medication to elderly patients (Kaufman et al, U.S. Pat. No. 5,126,957, incorporated herein by reference). This system dispenses certain medications to the patient on a pre-programmed schedule. It will dispense other medications to the patient on demand, providing that such medication is indicated by the health parameters of the patient and such medication has not been dispensed within a prescribed preceding time period.
Also of relevance to the present invention is the arena of hospital information systems. Hospital information systems are computer systems used in hospitals to keep track of large amounts of patient data, both administrative and clinical. Administrative data typically include admission, discharge, transfer, demographic and billing information. In the past, hospitals generally used separate computer systems for handling patient clinical data (e.g., monitoring and laboratory data). Hospital information systems may be used to maintain task lists which are updated automatically as actions are charted (Brimm et al., U.S. Pat. No. 5,072,383, incorporated herein by reference). Recently, systems have become available which are capable of integrating both types of patient data, that may be available at the patient's bedside. These systems make it possible to have various patient data and software available continuously at a patient's bedside, that is, at the point of care.